Mortality in the SuperMIX cohort of people who inject drugs in Melbourne, Australia: a prospective observational study
Bibliographic record
Abstract
AIMS: To measure mortality rates and factors associated with mortality risk among participants in the SuperMIX study, a prospective cohort study of people who inject drugs. DESIGN: A prospective observational study using self-reported behavioural and linked mortality data. SETTING: Melbourne, Australia. PARTICIPANTS/CASES: A total of 1209 people who inject drugs (67% male) followed-up between 2008 and 2019 for 6913 person-years (PY). MEASUREMENTS: We linked participant identifiers from SuperMIX to the Australian National Death Index and estimated all-cause and drug-related mortality rates and standardized mortality ratios (SMRs). We used Cox regression to examine associations between mortality and fixed and time-varying socio-demographic, alcohol and other drug use and health service-related exposures. FINDINGS: Between 2008 and 2019 there were 76 deaths in the SuperMIX cohort. Of those with a known cause of death (n = 68), 35 (51%) were drug-related, yielding an all-cause mortality rate of 1.1 per 100 PY [95% confidence interval (CI) = 0.88-1.37] with an estimated SMR of 16.64 (95% CI = 13.29-20.83) and overall accidental drug-induced mortality rate of 0.5 per 100 PY (95% CI = 0.36-0.71). Reports of recent use of ambulance services [adjusted hazard ratio (aHR) = 3.77, 95% CI =1.78-7.97] and four or more incarcerations (aHR = 2.78, 95% CI = 1.55-4.99) were associated with increased mortality risk. CONCLUSIONS: In Melbourne, Australia, mortality among people who inject drugs appears to be positively associated with recent ambulance attendance and experience of incarceration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".